https://github.com/annaanastasy/consumer-behavior-clustering
Segmented customer data into clusters using KMeans to uncover actionable insights into consumer behavior for targeted marketing strategies.
https://github.com/annaanastasy/consumer-behavior-clustering
cluster-analysis clustering data-science exploratory-data-analysis kmeans-clustering machine-learning-algorithms python unsupervised-learning
Last synced: about 1 month ago
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Segmented customer data into clusters using KMeans to uncover actionable insights into consumer behavior for targeted marketing strategies.
- Host: GitHub
- URL: https://github.com/annaanastasy/consumer-behavior-clustering
- Owner: AnnaAnastasy
- Created: 2024-11-15T14:46:27.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-11-22T14:51:34.000Z (over 1 year ago)
- Last Synced: 2025-07-01T03:03:43.354Z (11 months ago)
- Topics: cluster-analysis, clustering, data-science, exploratory-data-analysis, kmeans-clustering, machine-learning-algorithms, python, unsupervised-learning
- Language: Jupyter Notebook
- Homepage: https://www.kaggle.com/code/annastasy/consumer-behavior-cluster-analysis-kmeans
- Size: 826 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Consumer Behavior Analysis: Uncovering Insights with KMeans Clustering
This project explores customer segmentation using the KMeans clustering algorithm to identify distinct behavioral patterns, enabling businesses to design targeted marketing and retention strategies.
## Table of Contents
1. [Project Overview](#project-overview)
2. [Dataset](#dataset)
3. [Exploratory Data Analysis (EDA)](#exploratory-data-analysis-eda)
4. [Clustering Analysis](#clustering-analysis)
5. [Insights and Recommendations](#insights-and-recommendations)
6. [How to Run the Notebook](#how-to-run-the-notebook)
---
## 1. Project Overview
Understanding customer behavior is crucial for creating personalized marketing strategies. This project utilizes the KMeans clustering algorithm to segment customers into meaningful groups based on behavioral data.
### Key Objectives:
- Analyze customer data to identify distinct clusters.
- Provide actionable insights into customer behavior for improved marketing strategies.
---
## 2. Dataset
The dataset contains customer data including demographics, purchase history, and other relevant metrics.
### Key Information:
- **Source:** [Kaggle Dataset](https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis)
- **Size:** Several columns representing various behavioral and demographic attributes.
- **Target Analysis:** Unsupervised clustering (no target variable).
---
## 3. Exploratory Data Analysis (EDA)
- Addressed missing values and normalized the data for better clustering results.
- Explored patterns in features such as spending habits and demographics.
---
## 4. Clustering Analysis
### Methodology:
- Applied KMeans clustering to segment customers based on their similarities.
- Determined the optimal number of clusters using the Elbow Method and Silhouette Score.
---
## 5. Insights and Recommendations
### Key Results:
- Customers were segmented into **three distinct clusters** representing unique behavioral traits.
- Each cluster provides valuable insights for designing targeted marketing campaigns and retention strategies.
---
## 6. How to Run the Notebook
### Prerequisites
- Python 3.8 or higher
- Libraries: `numpy`, `pandas`, `matplotlib`, `seaborn`, `scikit-learn`.
### Setup
1. Install required libraries:
```bash
pip install numpy pandas matplotlib seaborn scikit-learn